We approached this problem by bridging work on inferred mappings for visualizations of categorical information [1]}, [2]}, [3]}, [4]}, [5]} and visualizations of continuous data [6]}, [7]}, [8]}, [9]} to understand both within a single framework of assignment inference. Doing so required broadening the notion of merit in assignment inference to include not only direct associations as in [3]}, [4]}, [5]}, but also relational associations (e.g., dark-is-more bias). Exp. 1 showed that direct and relational associations contribute independently to inferred mappings for colormaps. Exp. 2 showed that inferred mappings for colormaps were predicted by simulations of assignment inference (signed semantic distance) using merit from direct associations. Exp. 3 showed that simulating assignment inference using a weighted sum over merit from direct and relational associations was better at predicting inferred mappings than simulations using each source of merit alone.
